Table of Contents

• 1  Credit Card Users Churn Prediction
  • 1.1  Context
  • 1.2  Objective
  • 1.3  Key questions to be answered
  • 1.4  Data Description
• 2  Load and explore the dataset
  • 2.1  Import the necessary packages
  • 2.2  Read the dataset
  • 2.3  View the first and last 5 rows and sample of the dataset.
  • 2.4  Understand the shape of the dataset.
  • 2.5  Check the columns in the data
  • 2.6  Check the data types of the columns
  • 2.7  Fixing the data types
  • 2.8  Check for missing values
  • 2.9  Check for duplicate values
  • 2.10  Summary of the dataset
• 3  Data Pre-Processing
• 4  EDA
  • 4.1  Univariate analysis
    • 4.1.1  Observations on Customer_Age
    • 4.1.2  Observations on Dependent_count
    • 4.1.3  Observations on Months_on_book
    • 4.1.4  Observations on Total_Relationship_Count
    • 4.1.5  Observations on Months_Inactive_12_mon
    • 4.1.6  Observations on Contacts_Count_12_mon
    • 4.1.7  Observations on Credit_Limit
    • 4.1.8  Observations on Total_Revolving_Bal
    • 4.1.9  Observations on Avg_Open_To_Buy
    • 4.1.10  Observations on Total_Amt_Chng_Q4_Q1
    • 4.1.11  Observations on Total_Trans_Amt
    • 4.1.12  Observations on Total_Trans_Ct
    • 4.1.13  Observations on Total_Ct_Chng_Q4_Q1
    • 4.1.14  Observations on Avg_Utilization_Ratio
    • 4.1.15  Observations on Attrition_Flag
    • 4.1.16  Observations on Gender
    • 4.1.17  Observations on Education_Level
    • 4.1.18  Observations on Marital_Status
    • 4.1.19  Observations on Income_Category
    • 4.1.20  Observations on Card_Category
  • 4.2  Bivariate Analysis
    • 4.2.1  Correlation between numerical variables
    • 4.2.2  Target vs Dependent_count
    • 4.2.3  Target vs Months_on_book, Customer_Age
    • 4.2.4  Target vs Total_Relationship_Count
    • 4.2.5  Target vs Months_Inactive_12_mon, Contacts_Count_12_mon
    • 4.2.6  Target vs Credit_Limit, Avg_Open_To_Buy
    • 4.2.7  Target vs Total_Revolving_Bal, Avg_Utilization_Ratio
    • 4.2.8  Target vs Total_Amt_Chng_Q4_Q1
    • 4.2.9  Target vs Total_Trans_Amt, Total_Trans_Ct
    • 4.2.10  Target vs Total_Ct_Chng_Q4_Q1
    • 4.2.11  Target vs Gender
    • 4.2.12  Target vs Education_Level
    • 4.2.13  Target vs Marital_Status
    • 4.2.14  Target vs Income_Category
    • 4.2.15  Target vs Card_Category
• 5  Data Pre-Processing Contd.
  • 5.1  Missing Value Treatment
  • 5.2  Data Preparation for Modeling
  • 5.3  Creating Dummy Variables
• 6  Building the model
  • 6.1  Model evaluation criterion
    • 6.1.1  Model can make wrong predictions as
      • 6.1.1.1  Which case is more important?
      • 6.1.1.2  How to reduce this loss i.e need to reduce False Negatives?
  • 6.2  Model building using KFold and cross_val_score
  • 6.3  Model building with Oversampled train data (using SMOTE)
  • 6.4  Model building with Undersampled train data (using Random Under Sampler)
• 7  Hyperparameter tuning using RandomizedSearchCV
  • 7.1  XGBoost - Regular Train Data
  • 7.2  XGBoost - Oversampled Train Data
  • 7.3  XGBoost - Undersampled Train Data
  • 7.4  Adaboost - Undersampled Train Data
  • 7.5  Gradient Boosting Classifier - Undersampled Train Data
• 8  Comparing all models
  • 8.1  Performance on the test set
  • 8.2  Feature Importances
• 9  Pipelines for productionizing the model
  • 9.1  Column Transformer
• 10  Business Insights and Recommendations

Credit Card Users Churn Prediction

Context

Thera bank recently saw a steep decline in the number of users of their credit card. Customers’ leaving credit cards services would lead bank to loss, so the bank wants to analyze the data of customers and identify the customers who will leave their credit card services and reason for same – so that bank could improve upon those areas

Objective

• Build a classification model to predict if the customer is going to churn or not
• Optimize the model using appropriate techniques
• Generate a set of insights and recommendations that will help the bank

Key questions to be answered

• What are the different factors which influence a customer to leave a credit card?
• What business recommendations can we give based on the analysis?

Data Description

1. CLIENTNUM: Client number. Unique identifier for the customer holding the account
2. Attrition_Flag: Internal event (customer activity) variable - if the account is closed then "Attrited Customer" else "Existing Customer"
3. Customer_Age: Age in Years
4. Gender: Gender of the account holder
5. Dependent_count: Number of dependents
6. Education_Level: Educational Qualification of the account holder - Graduate, High School, Unknown, Uneducated, College(refers to a college student), Post-Graduate, Doctorate.
7. Marital_Status: Marital Status of the account holder
8. Income_Category: Annual Income Category of the account holder
9. Card_Category: Type of Card
10. Months_on_book: Period of relationship with the bank
11. Total_Relationship_Count: Total no. of products held by the customer
12. Months_Inactive_12_mon: No. of months inactive in the last 12 months
13. Contacts_Count_12_mon: No. of Contacts between the customer and bank in the last 12 months
14. Credit_Limit: Credit Limit on the Credit Card
15. Total_Revolving_Bal: The balance that carries over from one month to the next is the revolving balance
16. Avg_Open_To_Buy: Open to Buy refers to the amount left on the credit card to use (Average of last 12 months)
17. Total_Trans_Amt: Total Transaction Amount (Last 12 months)
18. Total_Trans_Ct: Total Transaction Count (Last 12 months)
19. Total_Ct_Chng_Q4_Q1: Ratio of the total transaction count in 4th quarter and the total transaction count in 1st quarter
20. Total_Amt_Chng_Q4_Q1: Ratio of the total transaction amount in 4th quarter and the total transaction amount in 1st quarter
21. Avg_Utilization_Ratio: Represents how much of the available credit the customer spent

Load and explore the dataset

Import the necessary packages

Let's start by importing libraries we need.

# To help with reading and manipulating data
import pandas as pd
import numpy as np

# To help with data visualization
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns

# To be used for missing value imputation
from sklearn.impute import SimpleImputer

# To help with model building
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import (
    AdaBoostClassifier,
    GradientBoostingClassifier,
    RandomForestClassifier,
    BaggingClassifier,
)
from xgboost import XGBClassifier

# To get different metric scores, and split data
from sklearn import metrics
from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    plot_confusion_matrix,
)


# To impute missing values
from sklearn.impute import KNNImputer

# To be used for data scaling and one hot encoding
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder

# To oversample and undersample data
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler

# To be used for tuning the model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

# To be used for creating pipelines and personalizing them
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer

# --------------------------------------------------------------------------------

# This will help in making the Python code more structured automatically (good coding practice)
%reload_ext nb_black

# Library to suppress warnings or deprecation notes
import warnings

warnings.filterwarnings("ignore")

# To define maximum number of columns to be displayed in a dataframe
pd.set_option("display.max_columns", None)

# To supress scientific notations for a dataframe
pd.set_option("display.float_format", lambda x: "%.2f" % x)
pd.set_option("display.max_colwidth", 0)

Read the dataset

customer_data = pd.read_csv("./datasets/BankChurners.csv")
df = customer_data.copy()

View the first and last 5 rows and sample of the dataset.

df.head()

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	5	1	3	12691.00	777	11914.00	1.33	1144	42	1.62	0.06
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	6	1	2	8256.00	864	7392.00	1.54	1291	33	3.71	0.10
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	4	1	0	3418.00	0	3418.00	2.59	1887	20	2.33	0.00
3	769911858	Existing Customer	40	F	4	High School	NaN	Less than $40K	Blue	34	3	4	1	3313.00	2517	796.00	1.41	1171	20	2.33	0.76
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	5	1	0	4716.00	0	4716.00	2.17	816	28	2.50	0.00

df.tail()

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
10122	772366833	Existing Customer	50	M	2	Graduate	Single	$40K - $60K	Blue	40	3	2	3	4003.00	1851	2152.00	0.70	15476	117	0.86	0.46
10123	710638233	Attrited Customer	41	M	2	NaN	Divorced	$40K - $60K	Blue	25	4	2	3	4277.00	2186	2091.00	0.80	8764	69	0.68	0.51
10124	716506083	Attrited Customer	44	F	1	High School	Married	Less than $40K	Blue	36	5	3	4	5409.00	0	5409.00	0.82	10291	60	0.82	0.00
10125	717406983	Attrited Customer	30	M	2	Graduate	NaN	$40K - $60K	Blue	36	4	3	3	5281.00	0	5281.00	0.54	8395	62	0.72	0.00
10126	714337233	Attrited Customer	43	F	2	Graduate	Married	Less than $40K	Silver	25	6	2	4	10388.00	1961	8427.00	0.70	10294	61	0.65	0.19

# Let's view sample of the data
df.sample(n=10, random_state=1)

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
6498	712389108	Existing Customer	43	F	2	Graduate	Married	Less than $40K	Blue	36	6	3	2	2570.00	2107	463.00	0.65	4058	83	0.77	0.82
9013	718388733	Existing Customer	38	F	1	College	NaN	Less than $40K	Blue	32	2	3	3	2609.00	1259	1350.00	0.87	8677	96	0.63	0.48
2053	710109633	Existing Customer	39	M	2	College	Married	$60K - $80K	Blue	31	6	3	2	9871.00	1061	8810.00	0.55	1683	34	0.48	0.11
3211	717331758	Existing Customer	44	M	4	Graduate	Married	$120K +	Blue	32	6	3	4	34516.00	2517	31999.00	0.77	4228	83	0.60	0.07
5559	709460883	Attrited Customer	38	F	2	Doctorate	Married	Less than $40K	Blue	28	5	2	4	1614.00	0	1614.00	0.61	2437	46	0.44	0.00
6106	789105183	Existing Customer	54	M	3	Post-Graduate	Single	$80K - $120K	Silver	42	3	1	2	34516.00	2488	32028.00	0.55	4401	87	0.78	0.07
4150	771342183	Attrited Customer	53	F	3	Graduate	Single	$40K - $60K	Blue	40	6	3	2	1625.00	0	1625.00	0.69	2314	43	0.43	0.00
2205	708174708	Existing Customer	38	M	4	Graduate	Married	$40K - $60K	Blue	27	6	2	4	5535.00	1276	4259.00	0.64	1764	38	0.90	0.23
4145	718076733	Existing Customer	43	M	1	Graduate	Single	$60K - $80K	Silver	31	4	3	3	25824.00	1170	24654.00	0.68	3101	73	0.78	0.04
5324	821889858	Attrited Customer	50	F	1	Doctorate	Single	abc	Blue	46	6	4	3	1970.00	1477	493.00	0.66	2493	44	0.57	0.75

Observations:

• CLIENTNUM column is just an ID for the customer
• Education_Level and Marital_Status have NaN value

Understand the shape of the dataset.

# checking shape of the data
print(f"There are {df.shape[0]} rows and {df.shape[1]} columns.")

There are 10127 rows and 21 columns.

Check the columns in the data

df.index

RangeIndex(start=0, stop=10127, step=1)

print(df.columns)

Index(['CLIENTNUM', 'Attrition_Flag', 'Customer_Age', 'Gender',
       'Dependent_count', 'Education_Level', 'Marital_Status',
       'Income_Category', 'Card_Category', 'Months_on_book',
       'Total_Relationship_Count', 'Months_Inactive_12_mon',
       'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',
       'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',
       'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio'],
      dtype='object')

Check the data types of the columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   CLIENTNUM                 10127 non-null  int64  
 1   Attrition_Flag            10127 non-null  object 
 2   Customer_Age              10127 non-null  int64  
 3   Gender                    10127 non-null  object 
 4   Dependent_count           10127 non-null  int64  
 5   Education_Level           8608 non-null   object 
 6   Marital_Status            9378 non-null   object 
 7   Income_Category           10127 non-null  object 
 8   Card_Category             10127 non-null  object 
 9   Months_on_book            10127 non-null  int64  
 10  Total_Relationship_Count  10127 non-null  int64  
 11  Months_Inactive_12_mon    10127 non-null  int64  
 12  Contacts_Count_12_mon     10127 non-null  int64  
 13  Credit_Limit              10127 non-null  float64
 14  Total_Revolving_Bal       10127 non-null  int64  
 15  Avg_Open_To_Buy           10127 non-null  float64
 16  Total_Amt_Chng_Q4_Q1      10127 non-null  float64
 17  Total_Trans_Amt           10127 non-null  int64  
 18  Total_Trans_Ct            10127 non-null  int64  
 19  Total_Ct_Chng_Q4_Q1       10127 non-null  float64
 20  Avg_Utilization_Ratio     10127 non-null  float64
dtypes: float64(5), int64(10), object(6)
memory usage: 1.6+ MB

Observations:

• Attrition Flag, Gender, Education Level, Marital Status, Income Category and Card_Category are of of object type
• CLIENTNUM, Customer_Age, Dependent_count, Months_on_book, Total_Relationship_Count, Months_Inactive_12_mon, Contacts_Count_12_mon, Total_Revolving_Bal, Total_Trans_Amt and Total_Trans_Ct are of int64 type
• Credit_Limit, Avg_Open_To_Buy, Total_Amt_Chng_Q4_Q1, Total_Ct_Chng_Q4_Q1, Avg_Utilization_Ratio are of float64 type
• 2 columns have less than 10127 values. i.e. columns have missing values.

Fixing the data types

cols = df.select_dtypes(["object"])
cols.columns

Index(['Attrition_Flag', 'Gender', 'Education_Level', 'Marital_Status',
       'Income_Category', 'Card_Category'],
      dtype='object')

for i in cols.columns:
    df[i] = df[i].astype("category")

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype   
---  ------                    --------------  -----   
 0   CLIENTNUM                 10127 non-null  int64   
 1   Attrition_Flag            10127 non-null  category
 2   Customer_Age              10127 non-null  int64   
 3   Gender                    10127 non-null  category
 4   Dependent_count           10127 non-null  int64   
 5   Education_Level           8608 non-null   category
 6   Marital_Status            9378 non-null   category
 7   Income_Category           10127 non-null  category
 8   Card_Category             10127 non-null  category
 9   Months_on_book            10127 non-null  int64   
 10  Total_Relationship_Count  10127 non-null  int64   
 11  Months_Inactive_12_mon    10127 non-null  int64   
 12  Contacts_Count_12_mon     10127 non-null  int64   
 13  Credit_Limit              10127 non-null  float64 
 14  Total_Revolving_Bal       10127 non-null  int64   
 15  Avg_Open_To_Buy           10127 non-null  float64 
 16  Total_Amt_Chng_Q4_Q1      10127 non-null  float64 
 17  Total_Trans_Amt           10127 non-null  int64   
 18  Total_Trans_Ct            10127 non-null  int64   
 19  Total_Ct_Chng_Q4_Q1       10127 non-null  float64 
 20  Avg_Utilization_Ratio     10127 non-null  float64 
dtypes: category(6), float64(5), int64(10)
memory usage: 1.2 MB

Check for missing values

# looking at which columns have the most missing values
round(df.isnull().sum() / df.isnull().count() * 100, 2)

CLIENTNUM                  0.00 
Attrition_Flag             0.00 
Customer_Age               0.00 
Gender                     0.00 
Dependent_count            0.00 
Education_Level            15.00
Marital_Status             7.40 
Income_Category            0.00 
Card_Category              0.00 
Months_on_book             0.00 
Total_Relationship_Count   0.00 
Months_Inactive_12_mon     0.00 
Contacts_Count_12_mon      0.00 
Credit_Limit               0.00 
Total_Revolving_Bal        0.00 
Avg_Open_To_Buy            0.00 
Total_Amt_Chng_Q4_Q1       0.00 
Total_Trans_Amt            0.00 
Total_Trans_Ct             0.00 
Total_Ct_Chng_Q4_Q1        0.00 
Avg_Utilization_Ratio      0.00 
dtype: float64

Observations:

• There are 2 columns with missing values
• 15% of Education_Level is missing
• 7.4% of Marital_Status is missing
• We will impute these values after we split the data into train, validation and test sets.

Check for duplicate values

df.duplicated().sum()

0

• There are no duplicate values in the dataset

Summary of the dataset

# let's view the statistical summary of the numerical columns in the data
df.describe().T

	count	mean	std	min	25%	50%	75%	max
CLIENTNUM	10127.00	739177606.33	36903783.45	708082083.00	713036770.50	717926358.00	773143533.00	828343083.00
Customer_Age	10127.00	46.33	8.02	26.00	41.00	46.00	52.00	73.00
Dependent_count	10127.00	2.35	1.30	0.00	1.00	2.00	3.00	5.00
Months_on_book	10127.00	35.93	7.99	13.00	31.00	36.00	40.00	56.00
Total_Relationship_Count	10127.00	3.81	1.55	1.00	3.00	4.00	5.00	6.00
Months_Inactive_12_mon	10127.00	2.34	1.01	0.00	2.00	2.00	3.00	6.00
Contacts_Count_12_mon	10127.00	2.46	1.11	0.00	2.00	2.00	3.00	6.00
Credit_Limit	10127.00	8631.95	9088.78	1438.30	2555.00	4549.00	11067.50	34516.00
Total_Revolving_Bal	10127.00	1162.81	814.99	0.00	359.00	1276.00	1784.00	2517.00
Avg_Open_To_Buy	10127.00	7469.14	9090.69	3.00	1324.50	3474.00	9859.00	34516.00
Total_Amt_Chng_Q4_Q1	10127.00	0.76	0.22	0.00	0.63	0.74	0.86	3.40
Total_Trans_Amt	10127.00	4404.09	3397.13	510.00	2155.50	3899.00	4741.00	18484.00
Total_Trans_Ct	10127.00	64.86	23.47	10.00	45.00	67.00	81.00	139.00
Total_Ct_Chng_Q4_Q1	10127.00	0.71	0.24	0.00	0.58	0.70	0.82	3.71
Avg_Utilization_Ratio	10127.00	0.27	0.28	0.00	0.02	0.18	0.50	1.00

Observations:

• CLIENTNUM is an ID variable and not useful for predictive modelling
• The mean and median are similar or closer in Customer_Age, Dependent_count, Months_on_book, Total_Relationship_Count, Months_Inactive_12_mon, Contacts_Count_12_mon, Total_Amt_Chng_Q4_Q1, Total_Trans_Ct, sliding fee scale and hence the distribution will be closer to normal distribution in these columns
• We see the max values for Total_Relationship_Count, Months_Inactive_12_mon and Contacts_Count_12_mon is 6 wile the min value is 1, 0 and 0 respectively.
• Dependent_count has min value 0 and max value of 5
• Credit_Limit has a wide range from 1438.30 to 34516.00. We need to check if this credit limit is outlier or not. Avg_Open_To_Buy also has a high max value compared to the min value and expected since some cards have high credit limit.
• Total_Ct_Chng_Q4_Q1 is the ratio or 4th quarter transaction count to the first quarter. Value greater than 1 implies that customer has been spending more in the 4th quarter while value less than 1 implies the opposite. 75 percentile value is less than 1 which is a trend that needs to be looked into.

df.describe(include=["category"]).T

	count	unique	top	freq
Attrition_Flag	10127	2	Existing Customer	8500
Gender	10127	2	F	5358
Education_Level	8608	6	Graduate	3128
Marital_Status	9378	3	Married	4687
Income_Category	10127	6	Less than $40K	3561
Card_Category	10127	4	Blue	9436

Observations:

• Attrition_Flag is the target variable with 84% records of Existing Customer.
• Majority of the customers are Female.
• Majority of the customers are graduates.
• Majority customers earn less than $40,000.
• Majority of the customers are Married.
• Blue is the most preferred card category.

Number of unique values in each column

df.nunique()

CLIENTNUM                   10127
Attrition_Flag              2    
Customer_Age                45   
Gender                      2    
Dependent_count             6    
Education_Level             6    
Marital_Status              3    
Income_Category             6    
Card_Category               4    
Months_on_book              44   
Total_Relationship_Count    6    
Months_Inactive_12_mon      7    
Contacts_Count_12_mon       7    
Credit_Limit                6205 
Total_Revolving_Bal         1974 
Avg_Open_To_Buy             6813 
Total_Amt_Chng_Q4_Q1        1158 
Total_Trans_Amt             5033 
Total_Trans_Ct              126  
Total_Ct_Chng_Q4_Q1         830  
Avg_Utilization_Ratio       964  
dtype: int64

Let's look at the unqiue values of all the categories

cols_cat = df.select_dtypes(["category"])

df.value_counts
for i in cols_cat:
    print("Unique values in", i, "are :")
    print(df[i].value_counts(dropna=False))
    print("*" * 50)

Unique values in Attrition_Flag are :
Existing Customer    8500
Attrited Customer    1627
Name: Attrition_Flag, dtype: int64
**************************************************
Unique values in Gender are :
F    5358
M    4769
Name: Gender, dtype: int64
**************************************************
Unique values in Education_Level are :
Graduate         3128
High School      2013
NaN              1519
Uneducated       1487
College          1013
Post-Graduate    516 
Doctorate        451 
Name: Education_Level, dtype: int64
**************************************************
Unique values in Marital_Status are :
Married     4687
Single      3943
NaN         749 
Divorced    748 
Name: Marital_Status, dtype: int64
**************************************************
Unique values in Income_Category are :
Less than $40K    3561
$40K - $60K       1790
$80K - $120K      1535
$60K - $80K       1402
abc               1112
$120K +           727 
Name: Income_Category, dtype: int64
**************************************************
Unique values in Card_Category are :
Blue        9436
Silver      555 
Gold        116 
Platinum    20  
Name: Card_Category, dtype: int64
**************************************************

Observations:

• We see abc as a unique value in the Income_Category column. We can mark these as NaN and treat as missing values
• We also need to treat the missing values in Education_Level and Marital_Status

Data Pre-Processing

# CLIENTNUM is unique for each candidate and might not add value to modeling
df.drop(["CLIENTNUM"], axis=1, inplace=True)

# Replace abc in Income_Category with Nan
df["Income_Category"] = df["Income_Category"].replace("abc", np.NaN)

• We will do missing value imputation after splitting the data into train, test and validation to avoid data leakage

EDA

Univariate analysis

# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="lightpink"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

Observations on Customer_Age

histogram_boxplot(df, "Customer_Age")

• Customer Age is looking normally distributed, with a couple of outliers
• Age greater than ~68 are outliers. Let's explore it further

df[df["Customer_Age"] > 68]

	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
251	Existing Customer	73	M	0	High School	Married	$40K - $60K	Blue	36	5	3	2	4469.00	1125	3344.00	1.36	1765	34	1.61	0.25
254	Existing Customer	70	M	0	High School	Married	Less than $40K	Blue	56	3	2	3	3252.00	1495	1757.00	0.58	1227	15	0.88	0.46

• The age of customers (70 & 73) are possible values for the customers and we don't need to treat them as outliers.

Observations on Dependent_count

histogram_boxplot(df, "Dependent_count")

• Dependent count is normally distributed
• On an average, there are 2.5 dependents per customer. Median is 2

Observations on Months_on_book

histogram_boxplot(df, "Months_on_book")

• Months on book is normally distributed. The mean and median are at ~36.
• Most customers have been with the bank for 36 months (3 years)
• From the boxplot we can see that months less than ~18 and greater than ~54 are outliers.
• Customers who have been with the bank for more than 54 months are loyal customers and would be less likely to attrite.
• We will not treat these outliers as they represent the real market trend

Observations on Total_Relationship_Count

histogram_boxplot(df, "Total_Relationship_Count")

• Distribution of Total Relationship Count looks slightly left skewed
• On an average, a customer held ~3.8 products. The median is 4.

Observations on Months_Inactive_12_mon

histogram_boxplot(df, "Months_Inactive_12_mon")

• Months inactive looks slightly right skewed
• On an average, a customer is inactive for ~2.4 months. The median is 2.
• Boxplot shows that values less than 1 and greater than 4 are outliers

df[df["Months_Inactive_12_mon"] < 1]

	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
29	Existing Customer	47	M	4	High School	Married	$40K - $60K	Blue	42	6	0	0	4785.00	1362	3423.00	0.74	1045	38	0.90	0.28
108	Existing Customer	62	F	1	NaN	Married	Less than $40K	Blue	56	6	0	0	1438.30	0	1438.30	0.81	1951	44	1.09	0.00
1470	Existing Customer	48	M	5	High School	Married	$40K - $60K	Silver	34	5	0	2	17657.00	2018	15639.00	0.55	1682	39	0.95	0.11
1721	Attrited Customer	61	M	2	Graduate	Married	$120K +	Blue	49	3	0	4	11374.00	2517	8857.00	1.01	1088	26	1.17	0.22
2126	Attrited Customer	46	F	3	High School	Single	Less than $40K	Silver	39	1	0	4	13092.00	1962	11130.00	0.69	936	25	0.25	0.15
2180	Existing Customer	26	F	2	High School	Single	NaN	Blue	13	5	0	3	5137.00	0	5137.00	0.70	2496	49	0.58	0.00
2671	Attrited Customer	49	M	3	Post-Graduate	Married	$80K - $120K	Blue	45	3	0	4	23870.00	0	23870.00	0.60	1885	39	0.34	0.00
3940	Attrited Customer	39	F	0	NaN	Married	$40K - $60K	Silver	34	3	0	3	15142.00	0	15142.00	0.76	2458	41	0.58	0.00
4224	Attrited Customer	57	F	3	Uneducated	Single	Less than $40K	Blue	51	3	0	3	1438.30	0	1438.30	0.43	2113	38	0.27	0.00
4302	Attrited Customer	50	F	1	High School	Single	NaN	Blue	42	5	0	4	10057.00	0	10057.00	0.79	2383	42	0.45	0.00
4511	Attrited Customer	63	M	0	Graduate	Single	NaN	Blue	55	5	0	2	18550.00	0	18550.00	0.91	2616	42	0.83	0.00
4700	Existing Customer	38	M	1	Graduate	Single	$80K - $120K	Blue	25	5	0	1	16665.00	1147	15518.00	0.80	4657	78	0.70	0.07
4702	Existing Customer	43	F	5	NaN	Single	Less than $40K	Blue	26	6	0	3	3139.00	1089	2050.00	1.03	4648	80	0.78	0.35
5116	Attrited Customer	57	M	3	Graduate	Married	$60K - $80K	Blue	49	5	0	3	8701.00	215	8486.00	0.53	1999	41	0.41	0.03
5251	Attrited Customer	46	F	2	NaN	Married	Less than $40K	Blue	42	1	0	4	2002.00	0	2002.00	0.80	2423	39	0.86	0.00
5592	Attrited Customer	54	F	1	Uneducated	Married	NaN	Blue	50	4	0	3	2912.00	0	2912.00	0.62	2465	54	1.08	0.00
5711	Attrited Customer	57	F	2	Uneducated	Single	Less than $40K	Blue	50	5	0	3	2475.00	0	2475.00	0.82	2574	43	0.65	0.00
6049	Attrited Customer	65	F	2	Graduate	Single	Less than $40K	Blue	56	3	0	3	6812.00	1926	4886.00	0.73	2288	48	0.78	0.28
6110	Existing Customer	41	F	3	College	Single	$40K - $60K	Blue	22	5	0	2	2577.00	1582	995.00	0.70	4734	75	0.60	0.61
6470	Existing Customer	35	F	1	High School	Married	Less than $40K	Blue	28	6	0	2	1438.30	977	461.30	1.19	4840	85	1.07	0.68
6474	Existing Customer	37	M	3	Uneducated	Married	$40K - $60K	Blue	25	5	0	3	1438.30	0	1438.30	1.02	5046	77	0.93	0.00
6557	Existing Customer	60	F	0	High School	Married	Less than $40K	Blue	50	6	0	1	3466.00	973	2493.00	0.65	4073	69	0.60	0.28
6597	Existing Customer	57	F	2	Post-Graduate	Married	Less than $40K	Blue	36	3	0	1	2712.00	1445	1267.00	0.82	4520	79	0.88	0.53
6605	Existing Customer	47	F	2	Doctorate	Single	Less than $40K	Blue	37	3	0	1	4254.00	1204	3050.00	0.54	5385	93	0.69	0.28
6647	Existing Customer	46	F	4	Graduate	Single	NaN	Blue	42	4	0	3	13376.00	1633	11743.00	0.98	4515	63	1.17	0.12
6663	Attrited Customer	58	F	2	High School	Married	Less than $40K	Blue	53	3	0	2	1944.00	0	1944.00	0.61	2412	40	0.48	0.00
6668	Existing Customer	65	M	0	Graduate	Single	$40K - $60K	Blue	36	4	0	1	1438.30	682	756.30	0.71	4427	63	1.17	0.47
8442	Attrited Customer	55	F	2	College	Single	NaN	Blue	49	1	0	6	1438.30	0	1438.30	0.61	2278	57	0.73	0.00
9633	Attrited Customer	59	F	1	High School	Single	Less than $40K	Blue	50	1	0	3	4085.00	0	4085.00	0.79	7597	71	0.69	0.00

• These outlier values represent actual variation in real-world scenarios
• We will not treat these outliers as they represent the real market trend

Observations on Contacts_Count_12_mon

histogram_boxplot(df, "Contacts_Count_12_mon")

• Contacts count looks slightly right skewed
• On an average, a customer contacted the bank 2.5 times. The median value is 2.
• Boxplot shows that values less than 1 and greater than 4 are outliers
• We will not treat these outliers as they represent the real market trend

Observations on Credit_Limit

histogram_boxplot(df, "Credit_Limit")

• Credit limit is heavily right skewed with many outliers
• Boxplot shows that values greater than ~23000 are outliers

df[df["Credit_Limit"] > 23000]

	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
6	Existing Customer	51	M	4	NaN	Married	$120K +	Gold	46	6	1	3	34516.00	2264	32252.00	1.98	1330	31	0.72	0.07
7	Existing Customer	32	M	0	High School	NaN	$60K - $80K	Silver	27	2	2	2	29081.00	1396	27685.00	2.20	1538	36	0.71	0.05
16	Existing Customer	48	M	4	Post-Graduate	Single	$80K - $120K	Blue	36	6	2	3	30367.00	2362	28005.00	1.71	1671	27	0.93	0.08
40	Existing Customer	45	M	3	Graduate	Single	$80K - $120K	Blue	41	2	2	2	32426.00	578	31848.00	1.04	1109	28	0.47	0.02
45	Existing Customer	49	M	4	Uneducated	Single	$80K - $120K	Blue	30	3	2	3	34516.00	0	34516.00	1.62	1444	28	1.33	0.00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10098	Attrited Customer	55	M	3	Graduate	Single	$120K +	Silver	36	4	3	4	34516.00	0	34516.00	1.01	9931	70	0.75	0.00
10100	Existing Customer	39	M	2	Graduate	NaN	$60K - $80K	Silver	36	4	2	2	29808.00	0	29808.00	0.67	16098	128	0.68	0.00
10104	Existing Customer	51	M	3	Graduate	Single	$60K - $80K	Silver	36	3	2	2	29663.00	1743	27920.00	0.67	14638	93	0.72	0.06
10110	Existing Customer	56	M	1	Graduate	Single	$80K - $120K	Silver	49	5	2	2	34516.00	1091	33425.00	0.64	15274	108	0.71	0.03
10112	Attrited Customer	33	M	2	College	Married	$120K +	Gold	20	2	1	4	34516.00	0	34516.00	1.00	9338	73	0.62	0.00

1041 rows × 20 columns

• These outliers seem to be valid credit limits and are tracked by the Avg_Open_To_Buy columns whose values are closer to the Creidt Limit
• We will not treat these outliers as they represent the real market trend

Observations on Total_Revolving_Bal

histogram_boxplot(df, "Total_Revolving_Bal")

• Majority of the customers do not have a revolving balance
• The average revolving balance is ~1100 and the median value is ~1200.
• More balance a customer has, lesser the possibility of a customer to attrite

Observations on Avg_Open_To_Buy

histogram_boxplot(df, "Avg_Open_To_Buy")

• Average Amount left of the credit card to use is heavily right skewed with many outliers
• Most of the customers have less than $3000 left to use
• Boxplot indicates that amount greater than ~22000 are outliers
• As discussed in Credit_Limit, these outliers represent real market trend and hence will not treat these outliers

Observations on Total_Amt_Chng_Q4_Q1

histogram_boxplot(df, "Total_Amt_Chng_Q4_Q1")

• The distribution is slightly right skewed with mean and median around ~0.75
• Boxplot indicates that ratio of less than ~0.3 and greater than ~1.3 are outliers.
• The outliers greater than 1.3 are customers who have spent more in the 4th quarter compared to 1st quarter and are more active and less likely to attrite

df[df["Total_Amt_Chng_Q4_Q1"] > 2.5]

	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio
2	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	4	1	0	3418.00	0	3418.00	2.59	1887	20	2.33	0.00
8	Existing Customer	37	M	3	Uneducated	Single	$60K - $80K	Blue	36	5	2	0	22352.00	2517	19835.00	3.35	1350	24	1.18	0.11
12	Existing Customer	56	M	1	College	Single	$80K - $120K	Blue	36	3	6	0	11751.00	0	11751.00	3.40	1539	17	3.25	0.00
773	Existing Customer	61	M	0	Post-Graduate	Married	NaN	Blue	53	6	2	3	14434.00	1927	12507.00	2.67	1731	32	3.57	0.13

• Out of 10127 values, only 2 of these are greater than 3
• we can cap the value of variable to the next highest value.

# Checking largest 6 ratios
df.Total_Amt_Chng_Q4_Q1.nlargest(6)

12    3.40
8     3.35
773   2.67
2     2.59
219   2.37
47    2.36
Name: Total_Amt_Chng_Q4_Q1, dtype: float64

# Capping values for the ratio at next highest value i.e. 2.67
df["Total_Amt_Chng_Q4_Q1"].clip(upper=2.67, inplace=True)

Observations on Total_Trans_Amt

histogram_boxplot(df, "Total_Trans_Amt")

• This distribution has 5 peaks 1750, 4750, 8000, 14000, 14750
• The outliers are customers who have spent more than $8000. The more money a customer spends on a credit card, the more active the customer is and is less likely to attrite
• We will not treat these outliers as they represent the real market trend

Observations on Total_Trans_Ct

histogram_boxplot(df, "Total_Trans_Ct")

• This distriution seems to be close to normally distributed
• Few outliers are customers who have made many transactions in the last year and are less likely to attrite

Observations on Total_Ct_Chng_Q4_Q1

histogram_boxplot(df, "Total_Ct_Chng_Q4_Q1")

• This distribution is right skewed
• As per the box plot, ratio less than 0.3 and greater than 1.3 are outliers
• Outliers with ratio greater than 1.3 are customers who have made more transactions in the fourth quarter compared to the first quarter and are less likely to attrite
• We will not treat these outliers as they represent the real market trend

Observations on Avg_Utilization_Ratio

histogram_boxplot(df, "Avg_Utilization_Ratio")

• This distribution is heavily right skewed
• Most of the customers have utilization ration of 0 which means they have not used their available credit

# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None, rotation=90):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=rotation, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

Observations on Attrition_Flag

labeled_barplot(df, "Attrition_Flag", perc=True)

• 16.1 % of the customer attrited

Observations on Gender

labeled_barplot(df, "Gender", perc=True)

• 52.9% of the customers are females

Observations on Education_Level

labeled_barplot(df, "Education_Level", perc=True)

• 39.9% of the customers are Graduates followed by 19.9% of customers who have completed high school

Observations on Marital_Status

labeled_barplot(df, "Marital_Status", perc=True)

• 46.3% of the customers are Married follwed by 38.9% Single customers

Observations on Income_Category

labeled_barplot(df, "Income_Category", perc=True)

• 35.2% customers earn less than 40,000 dollars.
• 17.7% customers earn between 40,000 and 60,000 dollars

Observations on Card_Category

labeled_barplot(df, "Card_Category", perc=True)

• Overwhelming majority of the customers (93.2%) use Blue card followd by Silver used by 5.5% of the customers

Bivariate Analysis

Correlation between numerical variables

plt.figure(figsize=(15, 7))
sns.heatmap(df.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

• There is perfect correlation between Credit Limit and Ave Open To Buy
• There is a strong correlation between Customer Age and Months on Book
• There is a strong correlation between Total Transaction Amount and Total Transaction Count
• There is a slight correlation between Total Revolving Balance and Avg Utilization Ratio
• There is a low -ve correlation between Total Relationship_Count and Total_Trans_Amt, Total Trans Ct
• There is also a low -ve correlation between Avg Utilization Ratio and Credit Limit, Ave Open To Buy

sns.pairplot(data=df, diag_kind="kde", hue="Attrition_Flag")
plt.show()

• There are overlaps i.e. no clear distinction in the distribution of variables for attrited customers and existing customers
• Let's explore this further with the help of other plots.

Let's check the variation in Attrition_Flag with some of the other variables.

### function to plot distributions wrt target
def distribution_plot_wrt_target(data, predictor, target):
    plt.figure(figsize=(5, 5))
    sns.boxplot(data=data, x=target, y=predictor)
    plt.title("Boxplot w.r.t " + target)
    plt.show()

### function to plot distributions for a set of columns wrt target
def distribution_plots_wrt_target(data, cols, target):
    plt.figure(figsize=(10, 10))

    for i, variable in enumerate(cols):
        plt.subplot(3, 2, i + 1)
        sns.boxplot(data[target], data[variable])
        plt.tight_layout()
        plt.title(variable)
    plt.show()

Target vs Dependent_count

distribution_plot_wrt_target(df, "Dependent_count", "Attrition_Flag")

• Customers with less dependents are less likely to attrite compared to customers with more dependents

Target vs Months_on_book, Customer_Age

cols = df[["Months_on_book", "Customer_Age"]].columns.tolist()
distribution_plots_wrt_target(df, cols, "Attrition_Flag")

• Months_on_book and Customer_Age don't seem to have any impact on the target variable

Target vs Total_Relationship_Count

distribution_plot_wrt_target(df, "Total_Relationship_Count", "Attrition_Flag")

• The median relationship count of existing customers is more than the median relationship count of Attrited customers
• Customers with less relationship count are more likely to attrite

Target vs Months_Inactive_12_mon, Contacts_Count_12_mon

cols = df[["Months_Inactive_12_mon", "Contacts_Count_12_mon"]].columns.tolist()
distribution_plots_wrt_target(df, cols, "Attrition_Flag")

• Customers with less inactive months are less likely to attrite. More inactive months increases the chances of attrition.
• Customers with more contacts with the bank are more likely to attrite.

Target vs Credit_Limit, Avg_Open_To_Buy

cols = df[["Credit_Limit", "Avg_Open_To_Buy"]].columns.tolist()
distribution_plots_wrt_target(df, cols, "Attrition_Flag")

• Existing customers tend to have higher credit limit than the attrited customers. The median income seems to be a tad bit higher for the Existing customers
• Avg amount left on the credit card to use don't seem to have any impact on the target variable

Target vs Total_Revolving_Bal, Avg_Utilization_Ratio

cols = df[["Total_Revolving_Bal", "Avg_Utilization_Ratio"]].columns.tolist()
distribution_plots_wrt_target(df, cols, "Attrition_Flag")

• Customers having low total revolving balance tend to attrite more. The median balance of existing customers are more than the 75 percentile balance of attrited customers.
• Customers with low average utilization rate tend to attrite more.

Target vs Total_Amt_Chng_Q4_Q1

distribution_plot_wrt_target(df, "Total_Amt_Chng_Q4_Q1", "Attrition_Flag")

• Customers with low ratio of total amount spent in 4th quarter to the 1st quarter tend to attrite

Target vs Total_Trans_Amt, Total_Trans_Ct

cols = df[["Total_Trans_Amt", "Total_Trans_Ct"]].columns.tolist()
distribution_plots_wrt_target(df, cols, "Attrition_Flag")

• Total transaction count and Total transaction amount are less for Attrited Customer. Less value means higher probability for the customer to attrite.

Target vs Total_Ct_Chng_Q4_Q1

distribution_plot_wrt_target(df, "Total_Ct_Chng_Q4_Q1", "Attrition_Flag")

• Customers with low ratio of total transaction count in 4th quarter to the 1st quarter tend to attrite

# function to plot stacked bar chart
def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 100)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 1, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()

Target vs Gender

stacked_barplot(df, "Gender", "Attrition_Flag")

Attrition_Flag  Attrited Customer  Existing Customer    All
Gender                                                     
All             1627               8500               10127
F               930                4428               5358 
M               697                4072               4769 
----------------------------------------------------------------------------------------------------

• Females tend to attrite more than males

Target vs Education_Level

stacked_barplot(df, "Education_Level", "Attrition_Flag")

Attrition_Flag   Attrited Customer  Existing Customer   All
Education_Level                                            
All              1371               7237               8608
Graduate         487                2641               3128
High School      306                1707               2013
Uneducated       237                1250               1487
College          154                859                1013
Doctorate        95                 356                451 
Post-Graduate    92                 424                516 
----------------------------------------------------------------------------------------------------

• Doctorate and Post-Graduate customers are much more likely to leave the credit card services more than the other educational categories

Target vs Marital_Status

stacked_barplot(df, "Marital_Status", "Attrition_Flag")

Attrition_Flag  Attrited Customer  Existing Customer   All
Marital_Status                                            
All             1498               7880               9378
Married         709                3978               4687
Single          668                3275               3943
Divorced        121                627                748 
----------------------------------------------------------------------------------------------------

• Single and Divorced customers are slightly more likely to Attrite than Married customers

Target vs Income_Category

stacked_barplot(df, "Income_Category", "Attrition_Flag")

Attrition_Flag   Attrited Customer  Existing Customer   All
Income_Category                                            
All              1440               7575               9015
Less than $40K   612                2949               3561
$40K - $60K      271                1519               1790
$80K - $120K     242                1293               1535
$60K - $80K      189                1213               1402
$120K +          126                601                727 
----------------------------------------------------------------------------------------------------

• Customers who earn less than 40000 dollars and who earn more than 120,000 dollars are more likely to Attrite
• Customers making between 60,000 dollars and 80,000 dollars are less likely to attrite

Target vs Card_Category

stacked_barplot(df, "Card_Category", "Attrition_Flag")

Attrition_Flag  Attrited Customer  Existing Customer    All
Card_Category                                              
All             1627               8500               10127
Blue            1519               7917               9436 
Silver          82                 473                555  
Gold            21                 95                 116  
Platinum        5                  15                 20   
----------------------------------------------------------------------------------------------------

• Platinum card holders are more likely to attrite and Silver card holders are least likely to attrite.

Data Pre-Processing Contd.

Missing Value Treatment

• We will use KNN imputer to impute missing values.
• KNNImputer: Each sample's missing values are imputed by looking at the n_neighbors nearest neighbors found in the training set. Default value for n_neighbors=5.
• KNN imputer replaces missing values using the average of k nearest non-missing feature values.
• Nearest points are found based on euclidean distance.

The values obtained might not be integer always which is not be the best way to impute categorical values

• To take care of that we will round off the obtained values to nearest integer value

df.isnull().sum()

Attrition_Flag              0   
Customer_Age                0   
Gender                      0   
Dependent_count             0   
Education_Level             1519
Marital_Status              749 
Income_Category             1112
Card_Category               0   
Months_on_book              0   
Total_Relationship_Count    0   
Months_Inactive_12_mon      0   
Contacts_Count_12_mon       0   
Credit_Limit                0   
Total_Revolving_Bal         0   
Avg_Open_To_Buy             0   
Total_Amt_Chng_Q4_Q1        0   
Total_Trans_Amt             0   
Total_Trans_Ct              0   
Total_Ct_Chng_Q4_Q1         0   
Avg_Utilization_Ratio       0   
dtype: int64

imputer = KNNImputer(n_neighbors=5)

# defining a list with names of columns that will be used for imputation
reqd_col_for_impute = [
    "Education_Level",
    "Marital_Status",
    "Income_Category",
]

df[reqd_col_for_impute].head()

	Education_Level	Marital_Status	Income_Category
0	High School	Married	$60K - $80K
1	Graduate	Single	Less than $40K
2	Graduate	Married	$80K - $120K
3	High School	NaN	Less than $40K
4	Uneducated	Married	$60K - $80K

data1 = df.copy()

# we need to pass numerical values for each categorical column for KNN imputation so we will label encode them
education_level = {
    "Uneducated": 0,
    "High School": 1,
    "College": 2,
    "Graduate": 3,
    "Post-Graduate": 4,
    "Doctorate": 5,
}
data1["Education_Level"] = (
    data1["Education_Level"].map(education_level).astype("object")
)

marital_status = {"Single": 0, "Married": 1, "Divorced": 2}
data1["Marital_Status"] = data1["Marital_Status"].map(marital_status).astype("object")

income_category = {
    "Less than $40K": 0,
    "$40K - $60K": 1,
    "$80K - $120K": 2,
    "$60K - $80K": 3,
    "$120K +": 4,
}
data1["Income_Category"] = (
    data1["Income_Category"].map(income_category).astype("object")
)

el = data1["Education_Level"].unique()
print(f"Education level: {el}")

ms = data1["Marital_Status"].unique()
print(f"Marital_Status: {ms}")

ic = data1["Income_Category"].unique()
print(f"Income_Category: {ic}")

Education level: [1 3 0 nan 2 4 5]
Marital_Status: [1 0 nan 2]
Income_Category: [3 0 2 1 4 nan]

• Values have been encoded.

Data Preparation for Modeling

X = data1.drop(["Attrition_Flag"], axis=1)
y = data1["Attrition_Flag"].apply(lambda x: 1 if x == "Attrited Customer" else 0)

# Splitting data into training, validation and test set:
# first we split data into 2 parts, say temporary and test

X_temp, X_test, y_temp, y_test = train_test_split(
    X, y, test_size=0.2, random_state=1, stratify=y
)

# then we split the temporary set into train and validation

X_train, X_val, y_train, y_val = train_test_split(
    X_temp, y_temp, test_size=0.25, random_state=1, stratify=y_temp
)
print(X_train.shape, X_val.shape, X_test.shape)

(6075, 19) (2026, 19) (2026, 19)

print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in validation data =", X_val.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 6075
Number of rows in validation data = 2026
Number of rows in test data = 2026

Imputing Missing Values

# Fit and transform the train data
X_train[reqd_col_for_impute] = imputer.fit_transform(X_train[reqd_col_for_impute])

# Transform the train data
X_val[reqd_col_for_impute] = imputer.transform(X_val[reqd_col_for_impute])

# Transform the test data
X_test[reqd_col_for_impute] = imputer.transform(X_test[reqd_col_for_impute])

# Checking that no column has missing values in train, validation or test sets
print(X_train.isna().sum())
print("-" * 30)
print(X_val.isna().sum())
print("-" * 30)
print(X_test.isna().sum())

Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64
------------------------------
Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64
------------------------------
Customer_Age                0
Gender                      0
Dependent_count             0
Education_Level             0
Marital_Status              0
Income_Category             0
Card_Category               0
Months_on_book              0
Total_Relationship_Count    0
Months_Inactive_12_mon      0
Contacts_Count_12_mon       0
Credit_Limit                0
Total_Revolving_Bal         0
Avg_Open_To_Buy             0
Total_Amt_Chng_Q4_Q1        0
Total_Trans_Amt             0
Total_Trans_Ct              0
Total_Ct_Chng_Q4_Q1         0
Avg_Utilization_Ratio       0
dtype: int64

• All missing values have been treated.
• Let's inverse map the encoded values.

## Function to inverse the encoding
def inverse_mapping(x, y):
    inv_dict = {v: k for k, v in x.items()}
    X_train[y] = np.round(X_train[y]).map(inv_dict).astype("category")
    X_val[y] = np.round(X_val[y]).map(inv_dict).astype("category")
    X_test[y] = np.round(X_test[y]).map(inv_dict).astype("category")

inverse_mapping(education_level, "Education_Level")
inverse_mapping(marital_status, "Marital_Status")
inverse_mapping(income_category, "Income_Category")

• Checking inverse mapped values/categories.

cols = X_train.select_dtypes(include=["object", "category"])
for i in cols.columns:
    print(X_train[i].value_counts())
    print("*" * 30)

F    3193
M    2882
Name: Gender, dtype: int64
******************************
Graduate         2223
High School      1260
College          1078
Uneducated       881 
Post-Graduate    379 
Doctorate        254 
Name: Education_Level, dtype: int64
******************************
Married     3168
Single      2477
Divorced    430 
Name: Marital_Status, dtype: int64
******************************
Less than $40K    2219
$40K - $60K       1401
$80K - $120K      1131
$60K - $80K       875 
$120K +           449 
Name: Income_Category, dtype: int64
******************************
Blue        5655
Silver      339 
Gold        69  
Platinum    12  
Name: Card_Category, dtype: int64
******************************

cols = X_val.select_dtypes(include=["object", "category"])
for i in cols.columns:
    print(X_val[i].value_counts())
    print("*" * 30)

F    1095
M    931 
Name: Gender, dtype: int64
******************************
Graduate         740
High School      412
College          348
Uneducated       306
Post-Graduate    121
Doctorate        99 
Name: Education_Level, dtype: int64
******************************
Married     1059
Single      811 
Divorced    156 
Name: Marital_Status, dtype: int64
******************************
Less than $40K    777
$40K - $60K       473
$80K - $120K      349
$60K - $80K       291
$120K +           136
Name: Income_Category, dtype: int64
******************************
Blue        1905
Silver      97  
Gold        21  
Platinum    3   
Name: Card_Category, dtype: int64
******************************

cols = X_test.select_dtypes(include=["object", "category"])
for i in cols.columns:
    print(X_test[i].value_counts())
    print("*" * 30)

F    1070
M    956 
Name: Gender, dtype: int64
******************************
Graduate         778
High School      392
College          338
Uneducated       300
Post-Graduate    120
Doctorate        98 
Name: Education_Level, dtype: int64
******************************
Married     1031
Single      833 
Divorced    162 
Name: Marital_Status, dtype: int64
******************************
Less than $40K    722
$40K - $60K       497
$80K - $120K      355
$60K - $80K       310
$120K +           142
Name: Income_Category, dtype: int64
******************************
Blue        1876
Silver      119 
Gold        26  
Platinum    5   
Name: Card_Category, dtype: int64
******************************

• Inverse mapping returned original labels.

Creating Dummy Variables

X_train = pd.get_dummies(X_train, drop_first=True)
X_val = pd.get_dummies(X_val, drop_first=True)
X_test = pd.get_dummies(X_test, drop_first=True)
print(X_train.shape, X_val.shape, X_test.shape)

(6075, 29) (2026, 29) (2026, 29)

• After encoding there are 29 columns.

Building the model

Model evaluation criterion

Model can make wrong predictions as

1. Predicting a customer will leave the credit card services and the customer doesn't leave - Loss of resources
2. Predicting a customer will not leave the credit card services and the customer attrites - Loss of opportunity

Which case is more important?

• Predicting a customer will not leave the credit card services and the customer attrites i.e. losing on an existing cutomer.

How to reduce this loss i.e need to reduce False Negatives?

• The bank would want Recall to be maximized, greater the Recall lesser the chances of false negatives.

# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

Model building using KFold and cross_val_score

Let's evaluate the model performance by using KFold and cross_val_score

• K-Folds cross-validation provides dataset indices to split data into train/validation sets. Split dataset into k consecutive stratified folds (without shuffling by default). Each fold is then used once as validation while the k - 1 remaining folds form the training set.

• Stratified K-fold is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

models = []  # Empty list to store all the models

# Appending models into the list
models.append(("Logistic Regression", LogisticRegression(random_state=1)))
models.append(("DTree", DecisionTreeClassifier(random_state=1)))
models.append(("Bagging", BaggingClassifier(random_state=1)))
models.append(("Random forest", RandomForestClassifier(random_state=1)))
models.append(("GBM", GradientBoostingClassifier(random_state=1)))
models.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))

results = []  # Empty list to store all model's CV scores
names = []  # Empty list to store name of the models

for name, model in models:
    model.fit(X_train, y_train)

# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")

for name, model in models:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train, y=y_train, scoring=scoring, cv=kfold
    )
    results.append(cv_result)
    names.append(name)
    print("{}: {}".format(name, cv_result.mean() * 100))

# loop through all models to get the training score
print("\n" "Training Performance:" "\n")
for name, model in models:
    scores = recall_score(y_train, model.predict(X_train)) * 100
    print("{}: {}".format(name, scores))

Cross-Validation Performance:

Logistic Regression: 42.71480900052329
DTree: 78.17791732077447
Bagging: 78.79016221873364
Random forest: 75.71585557299842
GBM: 81.7577184720042
Adaboost: 82.16745159602303
Xgboost: 85.96075353218211

Training Performance:

Logistic Regression: 48.87295081967213
DTree: 100.0
Bagging: 98.46311475409836
Random forest: 100.0
GBM: 87.60245901639344
Adaboost: 83.70901639344262
Xgboost: 100.0

# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))

fig.suptitle("Algorithm Comparison")
ax = fig.add_subplot(111)

plt.boxplot(results)
ax.set_xticklabels(names)

plt.show()

# Calculating different metrics on validation set
for name, model in models:
    scores = recall_score(y_val, model.predict(X_val)) * 100
    print("{}: {}".format(name, scores))

Logistic Regression: 53.37423312883436
DTree: 80.3680981595092
Bagging: 82.51533742331289
Random forest: 82.82208588957054
GBM: 86.1963190184049
Adaboost: 86.1963190184049
Xgboost: 88.95705521472392

res = []
nms = []

for name, model in models:
    model_val_perf = model_performance_classification_sklearn(model, X_val, y_val)
    res.append(model_val_perf)
    nms.append(name)

models_col = pd.Series(nms,)
model_val_df = pd.concat(res, axis=0, ignore_index=True,)
model_val_df.insert(0, "Models", models_col)
print("Validation performance comparison:")
model_val_df

Validation performance comparison:

	Models	Accuracy	Recall	Precision	F1
0	Logistic Regression	0.89	0.53	0.73	0.62
1	DTree	0.94	0.80	0.80	0.80
2	Bagging	0.96	0.83	0.90	0.86
3	Random forest	0.96	0.83	0.93	0.88
4	GBM	0.97	0.86	0.94	0.90
5	Adaboost	0.96	0.86	0.89	0.87
6	Xgboost	0.97	0.89	0.93	0.91

• We can see that Xgboost is giving the highest cross-validated recall and validation set recall followed by AdaBoost with one outlier
• We will tune XGBoost and see if the performance improves.

Model building with Oversampled train data (using SMOTE)

print("Before Oversampling, counts of Attrited Customer: {}".format(sum(y_train == 1)))
print(
    "Before Oversampling, counts of Existing Customer: {} \n".format(sum(y_train == 0))
)

sm = SMOTE(
    sampling_strategy=1, k_neighbors=5, random_state=1
)  # Synthetic Minority Over Sampling Technique
X_train_over, y_train_over = sm.fit_resample(X_train, y_train)


print(
    "After Oversampling, counts of Attrited Customer: {}".format(sum(y_train_over == 1))
)
print(
    "After Oversampling, counts of Existing Customer: {} \n".format(
        sum(y_train_over == 0)
    )
)


print("After Oversampling, the shape of train_X: {}".format(X_train_over.shape))
print("After Oversampling, the shape of train_y: {} \n".format(y_train_over.shape))

Before Oversampling, counts of Attrited Customer: 976
Before Oversampling, counts of Existing Customer: 5099 

After Oversampling, counts of Attrited Customer: 5099
After Oversampling, counts of Existing Customer: 5099 

After Oversampling, the shape of train_X: (10198, 29)
After Oversampling, the shape of train_y: (10198,) 

models_over = []  # Empty list to store all the models

# Appending models into the list
models_over.append(("Logistic Regression", LogisticRegression(random_state=1)))
models_over.append(("DTree", DecisionTreeClassifier(random_state=1)))
models_over.append(("Bagging", BaggingClassifier(random_state=1)))
models_over.append(("Random forest", RandomForestClassifier(random_state=1)))
models_over.append(("GBM", GradientBoostingClassifier(random_state=1)))
models_over.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models_over.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))

results_over = []  # Empty list to store all model's CV scores
names_over = []  # Empty list to store name of the models

for name, model in models_over:
    model.fit(X_train_over, y_train_over)


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")

for name, model in models_over:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train_over, y=y_train_over, scoring=scoring, cv=kfold
    )
    results_over.append(cv_result)
    names_over.append(name)
    print("{}: {}".format(name, cv_result.mean() * 100))

print("\n" "Training Performance:" "\n")
for name, model in models_over:
    scores = recall_score(y_train_over, model.predict(X_train_over)) * 100
    print("{}: {}".format(name, scores))

Cross-Validation Performance:

Logistic Regression: 79.25124593507668
DTree: 95.07764244068578
Bagging: 96.13654293905982
Random forest: 98.01924224056648
GBM: 97.1563047201216
Adaboost: 96.23462063922723
Xgboost: 98.29377128672861

Training Performance:

Logistic Regression: 82.56520886448322
DTree: 100.0
Bagging: 99.7254363600706
Random forest: 100.0
GBM: 97.92116101196314
Adaboost: 96.41106099235144
Xgboost: 100.0

# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))

fig.suptitle("Algorithm Comparison - Oversampling")
ax = fig.add_subplot(111)

plt.boxplot(results_over)
ax.set_xticklabels(names_over)

plt.show()

# Calculating different metrics on validation set
for name, model in models_over:
    scores = recall_score(y_val, model.predict(X_val)) * 100
    print("{}: {}".format(name, scores))

Logistic Regression: 81.59509202453987
DTree: 82.20858895705521
Bagging: 85.88957055214725
Random forest: 86.50306748466258
GBM: 88.65030674846625
Adaboost: 88.95705521472392
Xgboost: 90.79754601226993

res_over = []
nms_over = []

for name, model in models_over:
    model_val_perf = model_performance_classification_sklearn(model, X_val, y_val)
    res_over.append(model_val_perf)
    nms_over.append(name)

models_col = pd.Series(nms_over,)
model_val_over_df = pd.concat(res_over, axis=0, ignore_index=True,)
model_val_over_df.insert(0, "Models", models_col)
print("Validation performance comparison (Oversampling):")
model_val_over_df

Validation performance comparison (Oversampling):

	Models	Accuracy	Recall	Precision	F1
0	Logistic Regression	0.83	0.82	0.49	0.61
1	DTree	0.93	0.82	0.75	0.78
2	Bagging	0.94	0.86	0.78	0.82
3	Random forest	0.96	0.87	0.86	0.86
4	GBM	0.96	0.89	0.85	0.87
5	Adaboost	0.94	0.89	0.79	0.84
6	Xgboost	0.97	0.91	0.89	0.90

• We can see that Xgboost is giving the highest cross-validated recall and validation set recall followed by GBM and AdaBoost
• We will tune XGBoost, GBM and Adaboost with Oversampling and see if the performance improves.

Model building with Undersampled train data (using Random Under Sampler)

print("Before Undersampling, counts of Attrited Customer: {}".format(sum(y_train == 1)))
print(
    "Before Undersampling, counts of Existing Customer: {} \n".format(sum(y_train == 0))
)

rus = RandomUnderSampler(random_state=1)
X_train_under, y_train_under = rus.fit_resample(X_train, y_train)

print(
    "After Undersampling, counts of Attrited Customer: {}".format(
        sum(y_train_under == 1)
    )
)
print(
    "After Undersampling, counts of Existing Customer: {} \n".format(
        sum(y_train_under == 0)
    )
)

print("After Undersampling, the shape of train_X: {}".format(X_train_under.shape))
print("After Undersampling, the shape of train_y: {} \n".format(y_train_under.shape))

Before Undersampling, counts of Attrited Customer: 976
Before Undersampling, counts of Existing Customer: 5099 

After Undersampling, counts of Attrited Customer: 976
After Undersampling, counts of Existing Customer: 976 

After Undersampling, the shape of train_X: (1952, 29)
After Undersampling, the shape of train_y: (1952,) 

models_under = []  # Empty list to store all the models

# Appending models into the list
models_under.append(("Logistic Regression", LogisticRegression(random_state=1)))
models_under.append(("DTree", DecisionTreeClassifier(random_state=1)))
models_under.append(("Bagging", BaggingClassifier(random_state=1)))
models_under.append(("Random forest", RandomForestClassifier(random_state=1)))
models_under.append(("GBM", GradientBoostingClassifier(random_state=1)))
models_under.append(("Adaboost", AdaBoostClassifier(random_state=1)))
models_under.append(("Xgboost", XGBClassifier(random_state=1, eval_metric="logloss")))

results_under = []  # Empty list to store all model's CV scores
names_under = []  # Empty list to store name of the models

for name, model in models_under:
    model.fit(X_train_under, y_train_under)


# loop through all models to get the mean cross validated score
print("\n" "Cross-Validation Performance:" "\n")

for name, model in models_under:
    scoring = "recall"
    kfold = StratifiedKFold(
        n_splits=5, shuffle=True, random_state=1
    )  # Setting number of splits equal to 5
    cv_result = cross_val_score(
        estimator=model, X=X_train_under, y=y_train_under, scoring=scoring, cv=kfold
    )
    results_under.append(cv_result)
    names_under.append(name)
    print("{}: {}".format(name, cv_result.mean() * 100))

print("\n" "Training Performance:" "\n")
for name, model in models_under:
    scores = recall_score(y_train_under, model.predict(X_train_under)) * 100
    print("{}: {}".format(name, scores))

Cross-Validation Performance:

Logistic Regression: 80.12192569335426
DTree: 88.62899005756148
Bagging: 90.77969649398221
Random forest: 93.34222919937206
GBM: 93.95604395604396
Adaboost: 92.82993197278913
Xgboost: 95.18524332810048

Training Performance:

Logistic Regression: 78.27868852459017
DTree: 100.0
Bagging: 99.18032786885246
Random forest: 100.0
GBM: 98.05327868852459
Adaboost: 95.28688524590164
Xgboost: 100.0

# Plotting boxplots for CV scores of all models defined above
fig = plt.figure(figsize=(10, 7))

fig.suptitle("Algorithm Comparison - Undersampling")
ax = fig.add_subplot(111)

plt.boxplot(results_under)
ax.set_xticklabels(names_under)

plt.show()

# Calculating different metrics on validation set
for name, model in models_under:
    scores = recall_score(y_val, model.predict(X_val)) * 100
    print("{}: {}".format(name, scores))

Logistic Regression: 78.83435582822086
DTree: 92.94478527607362
Bagging: 92.02453987730061
Random forest: 92.63803680981594
GBM: 96.0122699386503
Adaboost: 96.31901840490798
Xgboost: 96.62576687116564

res_under = []
nms_under = []

for name, model in models_under:
    model_val_perf = model_performance_classification_sklearn(model, X_val, y_val)
    res_under.append(model_val_perf)
    nms_under.append(name)

models_col = pd.Series(nms_under,)
model_val_under_df = pd.concat(res_under, axis=0, ignore_index=True,)
model_val_under_df.insert(0, "Models", models_col)
print("Validation performance comparison (Undersampling):")
model_val_under_df

Validation performance comparison (Undersampling):

	Models	Accuracy	Recall	Precision	F1
0	Logistic Regression	0.82	0.79	0.46	0.58
1	DTree	0.89	0.93	0.61	0.74
2	Bagging	0.92	0.92	0.70	0.80
3	Random forest	0.93	0.93	0.73	0.82
4	GBM	0.94	0.96	0.73	0.83
5	Adaboost	0.93	0.96	0.70	0.81
6	Xgboost	0.94	0.97	0.76	0.85

• We can see that Xgboost is giving the high cross-validated recall and validation set recall with one outlier followed by GBM with one outlier and Adaboost
• We will tune XGBoost, Adaboost and GBM with Undersampling and see if the performance improves.

Hyperparameter tuning using RandomizedSearchCV

• Looking at the previous 18 models that we built, we picked highest performing "XGBoost" model from regular train data.
• We picked XGBoost, GBM and Adaboost from Oversampled data for tuning. The performance numbers of these 3 models are lower than the numbers of Undersampling. So we will pick the highest scoring model and tune it, which is XGBoost in this case
• We picked XGBoost, Adaboost and GBM with Undersampling for tuning since all the 3 models perform better than the Oversampled models on the validation set and perform similar on the train set

XGBoost - Regular Train Data

# defining model
model_tuned = XGBClassifier(random_state=1, eval_metric="logloss")

# Parameter grid to pass in RandomizedSearchCV
param_grid = {
    "n_estimators": np.arange(50, 150, 50),
    "scale_pos_weight": [2, 5, 10],
    "learning_rate": [0.01, 0.1, 0.2, 0.05],
    "gamma": [0, 1, 3, 5],
    "subsample": [0.8, 0.9, 1],
    "max_depth": np.arange(1, 5, 1),
    "reg_lambda": [5, 10],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling RandomizedSearchCV
xgb_tuned = RandomizedSearchCV(
    estimator=model_tuned,
    param_distributions=param_grid,
    n_iter=50,
    scoring=scorer,
    cv=5,
    random_state=1,
    n_jobs=-1,
)

# Fitting parameters in RandomizedSearchCV
xgb_tuned.fit(X_train, y_train)

print(
    "Best parameters are {} with CV score={}:".format(
        xgb_tuned.best_params_, xgb_tuned.best_score_
    )
)

Best parameters are {'subsample': 0.8, 'scale_pos_weight': 10, 'reg_lambda': 10, 'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.1, 'gamma': 0} with CV score=0.9682260596546313:

# building model with best parameters
xgb_tuned = XGBClassifier(
    random_state=1,
    eval_metric="logloss",
    n_estimators=50,
    scale_pos_weight=10,
    gamma=0,
    subsample=0.8,
    learning_rate=0.1,
    max_depth=2,
    reg_lambda=10,
)
# Fit the model on training data
xgb_tuned.fit(X_train, y_train)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
              gamma=0, gpu_id=-1, importance_type='gain',
              interaction_constraints='', learning_rate=0.1, max_delta_step=0,
              max_depth=2, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=50, n_jobs=16,
              num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=10,
              scale_pos_weight=10, subsample=0.8, tree_method='exact',
              validate_parameters=1, verbosity=None)

# Calculating different metrics on train set
xgboost_random_train = model_performance_classification_sklearn(
    xgb_tuned, X_train, y_train
)
print("Training performance:")
xgboost_random_train

Training performance:

	Accuracy	Recall	Precision	F1
0	0.85	0.98	0.52	0.68

# Calculating different metrics on validation set
xgboost_random_val = model_performance_classification_sklearn(xgb_tuned, X_val, y_val)
print("Validation performance:")
xgboost_random_val

Validation performance:

	Accuracy	Recall	Precision	F1
0	0.85	0.97	0.51	0.67

# creating confusion matrix
confusion_matrix_sklearn(xgb_tuned, X_val, y_val)

XGBoost - Oversampled Train Data

# defining model
model_tuned_over = XGBClassifier(random_state=1, eval_metric="logloss")

# Parameter grid to pass in RandomizedSearchCV
param_grid = {
    "n_estimators": np.arange(50, 150, 50),
    "scale_pos_weight": [2, 5, 10],
    "learning_rate": [0.01, 0.1, 0.2, 0.05],
    "gamma": [0, 1, 3, 5],
    "subsample": [0.8, 0.9, 1],
    "max_depth": np.arange(1, 5, 1),
    "reg_lambda": [5, 10],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling RandomizedSearchCV
xgb_tuned_over = RandomizedSearchCV(
    estimator=model_tuned_over,
    param_distributions=param_grid,
    n_iter=50,
    scoring=scorer,
    cv=5,
    random_state=1,
    n_jobs=-1,
)

# Fitting parameters in RandomizedSearchCV
xgb_tuned_over.fit(X_train_over, y_train_over)

print(
    "Best parameters are {} with CV score={}:".format(
        xgb_tuned_over.best_params_, xgb_tuned_over.best_score_
    )
)

/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)

Best parameters are {'subsample': 0.8, 'scale_pos_weight': 10, 'reg_lambda': 10, 'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.05, 'gamma': 1} with CV score=0.9984313725490196:

# building model with best parameters
xgb_tuned_over = XGBClassifier(
    random_state=1,
    eval_metric="logloss",
    n_estimators=50,
    scale_pos_weight=10,
    gamma=1,
    subsample=0.8,
    learning_rate=0.05,
    max_depth=2,
    reg_lambda=10,
)
# Fit the model on training data
xgb_tuned_over.fit(X_train_over, y_train_over)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
              gamma=1, gpu_id=-1, importance_type='gain',
              interaction_constraints='', learning_rate=0.05, max_delta_step=0,
              max_depth=2, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=50, n_jobs=16,
              num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=10,
              scale_pos_weight=10, subsample=0.8, tree_method='exact',
              validate_parameters=1, verbosity=None)

# Calculating different metrics on train set
xgboost_random_train_over = model_performance_classification_sklearn(
    xgb_tuned_over, X_train_over, y_train_over
)
print("Training performance:")
xgboost_random_train_over

Training performance:

	Accuracy	Recall	Precision	F1
0	0.77	1.00	0.69	0.82

# Calculating different metrics on validation set
xgboost_random_val_over = model_performance_classification_sklearn(
    xgb_tuned_over, X_val, y_val
)
print("Validation performance:")
xgboost_random_val_over

Validation performance:

	Accuracy	Recall	Precision	F1
0	0.63	1.00	0.30	0.46

# creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_over, X_val, y_val)

XGBoost - Undersampled Train Data

# defining model
model_tuned_under = XGBClassifier(random_state=1, eval_metric="logloss")

# Parameter grid to pass in RandomizedSearchCV
param_grid = {
    "n_estimators": np.arange(50, 150, 50),
    "scale_pos_weight": [2, 5, 10],
    "learning_rate": [0.01, 0.1, 0.2, 0.05],
    "gamma": [0, 1, 3, 5],
    "subsample": [0.8, 0.9, 1],
    "max_depth": np.arange(1, 5, 1),
    "reg_lambda": [5, 10],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling RandomizedSearchCV
xgb_tuned_under = RandomizedSearchCV(
    estimator=model_tuned_under,
    param_distributions=param_grid,
    n_iter=50,
    scoring=scorer,
    cv=5,
    random_state=1,
    n_jobs=-1,
)

# Fitting parameters in RandomizedSearchCV
xgb_tuned_under.fit(X_train_under, y_train_under)

print(
    "Best parameters are {} with CV score={}:".format(
        xgb_tuned_under.best_params_, xgb_tuned_under.best_score_
    )
)

/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)
/Users/sthirumurthy/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)

Best parameters are {'subsample': 0.9, 'scale_pos_weight': 10, 'reg_lambda': 5, 'n_estimators': 50, 'max_depth': 1, 'learning_rate': 0.01, 'gamma': 1} with CV score=1.0:

# building model with best parameters
xgb_tuned_under = XGBClassifier(
    random_state=1,
    eval_metric="logloss",
    n_estimators=50,
    scale_pos_weight=10,
    gamma=1,
    subsample=0.9,
    learning_rate=0.01,
    max_depth=1,
    reg_lambda=5,
)
# Fit the model on training data
xgb_tuned_under.fit(X_train_under, y_train_under)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',
              gamma=1, gpu_id=-1, importance_type='gain',
              interaction_constraints='', learning_rate=0.01, max_delta_step=0,
              max_depth=1, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=50, n_jobs=16,
              num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=5,
              scale_pos_weight=10, subsample=0.9, tree_method='exact',
              validate_parameters=1, verbosity=None)

# Calculating different metrics on train set
xgboost_random_train_under = model_performance_classification_sklearn(
    xgb_tuned_under, X_train_under, y_train_under
)
print("Training performance:")
xgboost_random_train_under

Training performance:

	Accuracy	Recall	Precision	F1
0	0.50	1.00	0.50	0.67

# Calculating different metrics on validation set
xgboost_random_val_under = model_performance_classification_sklearn(
    xgb_tuned_under, X_val, y_val
)
print("Validation performance:")
xgboost_random_val_under

Validation performance:

	Accuracy	Recall	Precision	F1
0	0.16	1.00	0.16	0.28

# creating confusion matrix
confusion_matrix_sklearn(xgb_tuned_under, X_val, y_val)

Adaboost - Undersampled Train Data

# defining model
ada = AdaBoostClassifier(random_state=1)

# Parameter grid to pass in RandomizedSearchCV
param_grid = {
    "n_estimators": np.arange(10, 110, 10),
    "learning_rate": [0.1, 0.01, 0.2, 0.05, 1],
    "base_estimator": [
        DecisionTreeClassifier(max_depth=1, random_state=1),
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.recall_score)

# Calling RandomizedSearchCV
ada_tuned_under = RandomizedSearchCV(
    estimator=ada,
    param_distributions=param_grid,
    n_jobs=-1,
    n_iter=50,
    scoring=scorer,
    cv=5,
    random_state=1,
)

# Fitting parameters in RandomizedSearchCV
ada_tuned_under.fit(X_train_under, y_train_under)

print(
    "Best parameters are {} with CV score={}:".format(
        ada_tuned_under.best_params_, ada_tuned_under.best_score_
    )
)

Best parameters are {'n_estimators': 90, 'learning_rate': 0.1, 'base_estimator': DecisionTreeClassifier(max_depth=3, random_state=1)} with CV score=0.9487807430664574:

# building model with best parameters
ada_tuned_under = AdaBoostClassifier(
    random_state=1,
    n_estimators=90,
    learning_rate=0.1,
    base_estimator=DecisionTreeClassifier(max_depth=3, random_state=1),
)

# Fit the model on training data
ada_tuned_under.fit(X_train_under, y_train_under)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,
                                                         random_state=1),
                   learning_rate=0.1, n_estimators=90, random_state=1)

# Calculating different metrics on train set
ada_random_train_under = model_performance_classification_sklearn(
    ada_tuned_under, X_train_under, y_train_under
)
print("Training performance:")
ada_random_train_under

Training performance:

	Accuracy	Recall	Precision	F1
0	0.99	0.99	0.98	0.99

# Calculating different metrics on validation set
ada_random_val_under = model_performance_classification_sklearn(
    ada_tuned_under, X_val, y_val
)
print("Validation performance:")
ada_random_val_under

Validation performance:

	Accuracy	Recall	Precision	F1
0	0.94	0.96	0.73	0.83

# creating confusion matrix
confusion_matrix_sklearn(ada_tuned_under, X_val, y_val)

Gradient Boosting Classifier - Undersampled Train Data

# defining model
gbc = GradientBoostingClassifier(
    init=AdaBoostClassifier(random_state=1), random_state=1
)

# Parameter grid to pass in RandomizedSearchCV
parameters = {
    "n_estimators": [100, 150, 200, 250],
    "subsample": [0.8, 0.9, 1],
    "max_features": [0.7, 0.8, 0.9, 1],
}

# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
gbc_tuned_under = RandomizedSearchCV(
    estimator=gbc, param_distributions=parameters, scoring=acc_scorer, cv=5
)
gbc_tuned_under = gbc_tuned_under.fit(X_train_under, y_train_under)

print(
    "Best parameters are {} with CV score={}:".format(
        gbc_tuned_under.best_params_, gbc_tuned_under.best_score_
    )
)

# Set the clf to the best combination of parameters
gbc_tuned_under = gbc_tuned_under.best_estimator_

# Fit the best algorithm to the data.
gbc_tuned_under.fit(X_train_under, y_train_under)

Best parameters are {'subsample': 0.9, 'n_estimators': 200, 'max_features': 0.9} with CV score=0.9559445316588174:

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                           max_features=0.9, n_estimators=200, random_state=1,
                           subsample=0.9)

# Calculating different metrics on train set
gbc_random_train_under = model_performance_classification_sklearn(
    gbc_tuned_under, X_train_under, y_train_under
)
print("Training performance:")
gbc_random_train_under

Training performance:

	Accuracy	Recall	Precision	F1
0	1.00	1.00	0.99	1.00

# Calculating different metrics on validation set
gbc_random_val_under = model_performance_classification_sklearn(
    gbc_tuned_under, X_val, y_val
)
print("Validation performance:")
gbc_random_val_under

Validation performance:

	Accuracy	Recall	Precision	F1
0	0.95	0.98	0.76	0.85

# creating confusion matrix
confusion_matrix_sklearn(gbc_tuned_under, X_val, y_val)

Comparing all models

# training performance comparison

models_train_comp_df = pd.concat(
    [
        xgboost_random_train.T,
        xgboost_random_train_over.T,
        xgboost_random_train_under.T,
        ada_random_train_under.T,
        gbc_random_train_under.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "XGBoost Random",
    "XGBoost Random Oversampled",
    "XGBoost Random Undersampled",
    "AdaBoost Random Undersampled",
    "GradientBoost Random Undersampled",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	XGBoost Random	XGBoost Random Oversampled	XGBoost Random Undersampled	AdaBoost Random Undersampled	GradientBoost Random Undersampled
Accuracy	0.85	0.77	0.50	0.99	1.00
Recall	0.98	1.00	1.00	0.99	1.00
Precision	0.52	0.69	0.50	0.98	0.99
F1	0.68	0.82	0.67	0.99	1.00

# Validation performance comparison

models_val_comp_df = pd.concat(
    [
        xgboost_random_val.T,
        xgboost_random_val_over.T,
        xgboost_random_val_under.T,
        ada_random_val_under.T,
        gbc_random_val_under.T,
    ],
    axis=1,
)
models_val_comp_df.columns = [
    "XGBoost Random",
    "XGBoost Random Oversampled",
    "XGBoost Random Undersampled",
    "AdaBoost Random Undersampled",
    "GradientBoost Random Undersampled",
]
print("Validation performance comparison:")
models_val_comp_df

Validation performance comparison:

	XGBoost Random	XGBoost Random Oversampled	XGBoost Random Undersampled	AdaBoost Random Undersampled	GradientBoost Random Undersampled
Accuracy	0.85	0.63	0.16	0.94	0.95
Recall	0.97	1.00	1.00	0.96	0.98
Precision	0.51	0.30	0.16	0.73	0.76
F1	0.67	0.46	0.28	0.83	0.85

• The Gradient Boost model tuned on the undersampled train data using Random search is giving the best validation recall of 0.97 along with high accuracy and F1 scores.
• Let's check the model's performance on test set and then see the feature importance from the tuned Gradient Boost model

Performance on the test set

# Calculating different metrics on the test set
gbc_random_test_under = model_performance_classification_sklearn(
    gbc_tuned_under, X_test, y_test
)
print("Test performance:")
gbc_random_test_under

Test performance:

	Accuracy	Recall	Precision	F1
0	0.94	0.96	0.75	0.84

• The performance on test data is generalised

Feature Importances

feature_names = X_train_under.columns
importances = gbc_tuned_under.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

• Total transaction count is the most important feature followed by Total transaction amount and Total Revolving Balance

Pipelines for productionizing the model

Now, we have a final model. let's use pipelines to put the model into production

Column Transformer

• We know that we can use pipelines to standardize the model building, but the steps in a pipeline are applied to each and every variable - how can we personalize the pipeline to perform different processing on different columns

• Column transformer allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space. This is useful for heterogeneous or columnar data, to combine several feature extraction mechanisms or transformations into a single transformer.

• We will create 2 different pipelines, one for numerical columns and one for categorical columns
• For numerical columns, we will do missing value imputation as pre-processing

• For categorical columns, we will do one hot encoding and missing value imputation as pre-processing

• We are doing missing value imputation for the whole data, so that if there is any missing value in the data in future that can be taken care of.

# creating a list of numerical variables
numerical_features = [
    "Customer_Age",
    "Dependent_count",
    "Months_on_book",
    "Total_Relationship_Count",
    "Months_Inactive_12_mon",
    "Contacts_Count_12_mon",
    "Credit_Limit",
    "Total_Revolving_Bal",
    "Avg_Open_To_Buy",
    "Total_Amt_Chng_Q4_Q1",
    "Total_Trans_Amt",
    "Total_Trans_Ct",
    "Total_Ct_Chng_Q4_Q1",
    "Avg_Utilization_Ratio",
]

# creating a transformer for numerical variables, which will apply simple imputer on the numerical variables
numeric_transformer = Pipeline(steps=[("imputer", SimpleImputer(strategy="median"))])


# creating a list of categorical variables
categorical_features = ["Education_Level", "Marital_Status", "Gender", "Income_Category", "Card_Category"]

# creating a transformer for categorical variables, which will first apply simple imputer and 
#then do one hot encoding for categorical variables
categorical_transformer = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("onehot", OneHotEncoder(handle_unknown="ignore")),
    ]
)
# handle_unknown = "ignore", allows model to handle any unknown category in the test data

# combining categorical transformer and numerical transformer using a column transformer

preprocessor = ColumnTransformer(
    transformers=[
        ("num", numeric_transformer, numerical_features),
        ("cat", categorical_transformer, categorical_features),
    ],
    remainder="passthrough",
)
# remainder = "passthrough" has been used, it will allow variables that are present in original data 
# but not in "numerical_columns" and "categorical_columns" to pass through the column transformer without any changes

# Separating target variable and other variables
X = data1.drop(columns="Attrition_Flag")
Y = data1["Attrition_Flag"]

• Now we already know the best model we need to process with, so we don't need to divide data into 3 parts

# Splitting the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1, stratify=Y
)
print(X_train.shape, X_test.shape)

(7088, 19) (3039, 19)

# Creating new pipeline with best parameters
model = Pipeline(
    steps=[
        ("pre", preprocessor),
        (
            "GBC",
            GradientBoostingClassifier(
                init=AdaBoostClassifier(random_state=1),
                max_features=0.7,
                n_estimators=200,
                random_state=1,
                subsample=0.9,
            ),
        ),
    ]
)
# Fit the model on training data
model.fit(X_train, y_train)

Pipeline(steps=[('pre',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median'))]),
                                                  ['Customer_Age',
                                                   'Dependent_count',
                                                   'Months_on_book',
                                                   'Total_Relationship_Count',
                                                   'Months_Inactive_12_mon',
                                                   'Contacts_Count_12_mon',
                                                   'Credit_Limit',
                                                   'Total_Revolving_Bal',
                                                   'Avg_Open_To_Buy',
                                                   'Total_Amt_Chng_Q4_...
                                                 ('cat',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='most_frequent')),
                                                                  ('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Education_Level',
                                                   'Marital_Status', 'Gender',
                                                   'Income_Category',
                                                   'Card_Category'])])),
                ('GBC',
                 GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                                            max_features=0.7, n_estimators=200,
                                            random_state=1, subsample=0.9))])

# transforming and predicting on test data
model.predict(X_test)

array(['Existing Customer', 'Existing Customer', 'Existing Customer', ...,
       'Attrited Customer', 'Existing Customer', 'Attrited Customer'],
      dtype=object)

Business Insights and Recommendations

• We have been able to build a predictive model: a) that the bank can deploy to identify customers who are at the risk of attrition. b) that bank can use to find the drivers of attrition. c) based on which the bank can take appropriate actions to build better retention policies.
• Factors that influence attrition - Total transaction count, Total transaction amount and Total Revolving Balance
• Total transaction count - lesser the transaction count, higher the chances of attrition. The bank should announce a weekly and monthly giveaway - everytime a customer purchases using the credit card, they get an entry to this giveaway. This will incentivize the customer to make more transactions and hence improve retention.
• Total transaction amount - increasing the number of transactions will automatically increase the total transaction amount. Higher the total transaction amount, less likely the customer will attrite. A promotional giveaway would increase the total transaction amount by encouraging the customer to spend more.
• Banks should reduce penalties and interest rate on the late payment for the card. This would encourage customers to have more total revolving balance and hence increased revenue for the bank.
• Platinum card holders and customers earning more than $120,000 dollars are more likely to attrite. Bank should target the rich clients with more benefits for the Platinum card. They should also be given higher credit limit since lower the credit limit, higher the chances of attrition.
• Customers with more contacts with the bank are more likely to attrite. This could be due to lack of feedback options for customers. The company should create easy mechanisms to gather feedback from the customers and use it to identify major concerns if any.